COCO.
To further validate the generalization ability of the proposed method on different tasks, we conducted experiments on the challenging COCO object detection benchmark.
Experiments are performed on FPN [1]} with backbone ResNet18 and ResNet50 as students separately.
The teacher is an FPN with backbone ResNet152.
Knowledge distillation is applied to the output of the last residual feature map of the backbone network and \(L_2\)  loss is used as the distillation method.
Performance has been shown in Table REF .
All the results state that the knowledge discrepancy also exists in distillation of object detection tasks.
Our method shows a consistent performance improvement on the detection task.
In the case of ResNet18, the results increases from 33.6 to 36.8 on AP which makes ResNet18 achieve comparable performance with ResNet50.
Experiments on a larger student ResNet50 also shows a significant improvement from 37.7 to 40.8, outperforming the baseline by 3.1 points on AP.
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